Bayesian Prediction of Severe Outcomes in the LabMarCS: Laboratory Markers of COVID-19 Severity - Bristol Cohort [article]

Brian Sullivan, Edward Barker, Philip Williams, Louis MacGregor, Ranjeet Bhamber, Matt Thomas, Stefan Gurney, Catherine Hyams, Alastair Whiteway, Jennifer A Cooper, Chris McWilliams, Katy Turner (+2 others)
2022 medRxiv   pre-print
Objectives: To develop cross-validated prediction models for severe outcomes in COVID-19 using blood biomarker and demographic data; Demonstrate best practices for clinical data curation and statistical modelling decisions, with an emphasis on Bayesian methods. Design: Retrospective observational cohort study. Setting: Multicentre across National Health Service (NHS) trusts in Southwest region, England, UK. Participants: Hospitalised adult patients with a positive SARS-CoV 2 by PCR during the
more » ... rst wave (March - October 2020). 843 COVID-19 patients (mean age 71, 45% female, 32% died or needed ICU stay) split into training (n=590) and validation groups (n=253) along with observations on demographics, coinfections, and 30 laboratory blood biomarkers. Primary outcome measures: ICU admission or death within 28-days of admission to hospital for COVID-19 or a positive PCR result if already admitted. Results: Predictive regression models were fit to predict primary outcomes using demographic data and initial results from biomarker tests collected within 3 days of admission or testing positive if already admitted. Using all variables, a standard logistic regression yielded an internal validation median AUC of 0.7 (95% Interval [0.64,0.81]), and an external validation AUC of 0.67 [0.61, 0.71], a Bayesian logistic regression using a horseshoe prior yielded an internal validation median AUC of 0.78 [0.71, 0.85], and an external validation median AUC of 0.70 [0.68, 0.71]. Variable selection performed using Bayesian predictive projection determined a four variable model using Age, Urea, Prothrombin time and Neutrophil-Lymphocyte ratio, with a median AUC of 0.74 [0.67, 0.82], and external validation AUC of 0.70 [0.69, 0.71]. Conclusions: Our study reiterates the predictive value of previously identified biomarkers for COVID-19 severity assessment. Given the small data set, the full and reduced models have decent performance, but would require improved external validation for clinical application. The study highlights a variety of challenges present in complex medical data sets while maintaining best statistical practices with an emphasis on showcasing recent Bayesian methods.
doi:10.1101/2022.09.16.22279985 fatcat:w5fajbm23be4fj5yhwqhd2ogiq